R Markdown

For this report we will be looking at grad school admissions

This will give a glimpse of the data

Data summary
Name df
Number of rows 400
Number of columns 4
_______________________
Column type frequency:
numeric 4
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Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
admit 0 1 0.32 0.47 0.00 0.00 0.0 1.00 1 ▇▁▁▁▃
gre 0 1 587.70 115.52 220.00 520.00 580.0 660.00 800 ▁▂▇▇▅
gpa 0 1 3.39 0.38 2.26 3.13 3.4 3.67 4 ▁▃▆▇▆
rank 0 1 2.48 0.94 1.00 2.00 2.0 3.00 4 ▃▇▁▆▃

Here is a data table you can filter the categories using a slider to further explore the data set

Plots

GPA vs GRE

GPA vs GRE by rank in the class

This shows that those ranked 2nd got in the most

models

model 1

## # Indices of model performance
## 
## AIC     |     BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
## ----------------------------------------------------------------------------------------------
## 466.598 | 486.555 |     0.105 | 0.440 | 1.075 |    0.571 |   -50.481 |           0.004 | 0.612
## enrolled ~ gpa * gre + rank
term estimate std.error statistic p.value
(Intercept) -13.1963285 6.0461191 -2.182611 0.0290644
gpa 3.6610449 1.7797212 2.057089 0.0396777
gre 0.0185074 0.0098841 1.872434 0.0611466
rank -0.5657570 0.1274493 -4.439074 0.0000090
gpa:gre -0.0047619 0.0028747 -1.656468 0.0976271

model 2

## # Indices of model performance
## 
## AIC     |     BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
## ----------------------------------------------------------------------------------------------
## 472.250 | 504.182 |     0.106 | 0.440 | 1.079 |    0.570 |   -50.593 |           0.005 | 0.613
## enrolled ~ gpa * gre * rank
term estimate std.error statistic p.value
(Intercept) -15.0800147 16.4144813 -0.9187019 0.3582515
gpa 4.3613309 4.8453994 0.9000973 0.3680685
gre 0.0188177 0.0265689 0.7082616 0.4787828
rank 0.0472831 6.6068136 0.0071567 0.9942898
gpa:gre -0.0051014 0.0077469 -0.6585106 0.5102101
gpa:rank -0.2413823 1.9341272 -0.1248016 0.9006806
gre:rank 0.0002152 0.0108790 0.0197833 0.9842162
gpa:gre:rank 0.0000418 0.0031475 0.0132934 0.9893937

comparing the models performance

## # Comparison of Model Performance Indices
## 
## Name | Model | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP | AIC weights | BIC weights | Performance-Score
## -----------------------------------------------------------------------------------------------------------------------------------------
## mod1 |   glm |     0.105 | 0.440 | 1.075 |    0.571 |   -50.481 |           0.004 | 0.612 |       0.944 |       1.000 |            55.56%
## mod2 |   glm |     0.106 | 0.440 | 1.079 |    0.570 |   -50.593 |           0.005 | 0.613 |       0.056 |    1.49e-04 |            44.44%

Predictions

Percent chance of getting enrolled based on GRE

Percent chance of getting enrolled based on GPA

To access the link open in web browser If your feeling down on your chances click HERE